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通过大型语言模型和计算式文本挖掘,推进心理社会残疾和心理社会康复研究

Soheyla Amirian¹, Ashutosh Kekre¹, Boby John Loganathan¹

¹School of Computing, University of Georgia, Athens, GA, 30602 USA.

Global mental health (Cambridge, England)

|January 8, 2025

概括

这项研究使用人工智能驱动的科学文献文本挖掘来分析心理残疾研究的趋势. 它的目的是提高对患有这些疾病的个人的理解和照顾.

科学领域:

医疗保健和医疗信息学
人工智能在心理健康中的作用
心理社会康复研究心理社会康复研究

背景情况:

心理社会残疾研究是一个重要的医疗保健领域,越来越多的科学出版物.
现有的文献提供了关于诊断,预防,治疗和流行病学的见解.
对这一不断增长的研究成果进行有效的分析对于推进患者护理至关重要.

研究的目的:

应用先进的计算文本挖掘技术来分析关于心理康复和残疾的科学文献.
创建一个全面的文本数据集,并采用复杂的文本挖掘策略.
识别当前的趋势,并提高科学界关于心理社会挑战的知识.

主要方法:

利用文本挖掘,这是人工智能 (AI) 的一个子领域.
使用词嵌入和大型语言模型 (LLM) 来进行数据分析.
利用PubMed的科学文献库.

主要成果:

从关于心理社会残疾的广泛科学文章中提取有价值的见解.
为心理健康研究量身定制的先进计算文本挖掘策略的开发.
为未来的研究建立一个广泛的文本数据集.

结论:

计算式文本挖掘,特别是LLM,可以显著加快心理康复和残疾方面的研究.
这种方法提高了对当前趋势的理解,并有助于改善对心理社会挑战的人的护理.
该研究为心理健康研究和实践中数据驱动的进步提供了基础.

关键词:

计算式文本挖掘是一种计算式的文本挖掘.大型语言模型 (LLM)心理社会残疾研究研究心理社会康复心理社会康复

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